"""
 @time: 2019/6/6 19:42
 
 @Author: Unicorn
"""
# 手写字母分类 案列
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

# number 1 to 10 data  (example data)
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)  # 第一次使用会自动下载,以后就不会下载了,使用本地已存在的


def compute_accuracy(v_xs, v_ys):
    """
    计算精确度
    :param v_xs: 测试x值
    :param v_ys: 测试label
    :return: 精确度
    """
    global prediction  # 将预测 存为全局变量
    label_prediction = sess.run(prediction, feed_dict={xs: v_xs})  # 生成预测值
    correct_prediction = tf.equal(tf.argmax(label_prediction, 1), tf.argmax(v_ys, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    result = sess.run(accuracy, feed_dict={xs: v_xs, ys: v_ys})
    return result


def add_layer(inputs, in_size, out_size, activation_function=None):
    # add one more layer adn return the output of this layer
    Weights = tf.Variable(tf.random_normal([in_size, out_size]))
    biases = tf.Variable(tf.zeros([1, out_size]) + 0.1, )
    Wx_plus_b = tf.matmul(inputs, Weights) + biases
    if activation_function is None:
        outputs = Wx_plus_b
    else:
        outputs = activation_function(Wx_plus_b)
    return outputs


# 定义placeholder 网络的输入
xs = tf.placeholder(tf.float32, [None, 784])  # 原始图片的大小是28*28*像素
ys = tf.placeholder(tf.float32, [None, 10])  # 有10分类
# 定义输出层
prediction = add_layer(xs, 784, 10, activation_function=tf.nn.softmax)
# loss
cross_entropy = tf.reduce_mean(- tf.reduce_sum(ys * tf.log(prediction), reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
# 开启会话
sess = tf.Session()
sess.run(tf.initialize_all_variables())

# 训练次数
for i in range(1000):
    batch_xs, batch_ys = mnist.train.next_batch(100)  # 每次学习的批数量
    sess.run(train_step, feed_dict={xs: batch_xs, ys: batch_ys})
    if i % 50 == 0:
        # 每50次计算一次精确度
        print(compute_accuracy(mnist.test.images, mnist.test.labels))
